This thesis describes the parameters of electrical energy with a focus on low voltage characteristics and their measurement with respect to the parameters required by standards. In addition, one chapter deals with monitoring of the quality electric energy. Measurement organizations, calculation of power quality and the data collection for its calculation is referred here. For the illustration, the example of a distributed monitoring system from practice is described. The second part deals with the design and implementation of a module for measuring power quality , namely by measuring the voltage characteristics. It describes the selection of the chosen solution, hardware part and firmware for the MCU. To display the measured data, application was created by the Java programming language. The application allows the visualization of the voltage measured Low voltage grid with single phase voltage RMS value 230.

This thesis deals with steady running and short-circuit conditions in the 110 kV network, E. ON, fed from 110 kV Otrokovice. Substation Otrokovice ensures system transformation from 400 kV to 110 kV system through three transformers. In order to increase reliability of power supply and improved economy is considered to bridge the transition from the operation of two transformers in bridge operations of the three transformers. However, this brings with it significant worsening of short-circuit conditions in the 110 kV network. The task of this work is to evaluate and compare the two variants of operation in terms of steady running and short-circuit conditions in the network at 110 kV and then implement operational and technical measures. The introduction describes the electricity system and its parts. The theoretical part of the paper discusses ways of dealing with steady running and short circuit conditions of long lines. Practical part will deal with applications of knowledge on the network. The conclusion summarizes the most relevant information and the results assessed both variants.

<p>As the world population continues to grow, it will be challenging to manage resources, reduce environmental pollution and maintain growing demand for food production. Controlled environment agriculture (CEA) is a novel solution to reduce freshwater use in agriculture, minimize environmental pollution from agriculture sector, and meet the growing food demand. CEA allows for the year-round cultivation in inhospitable climatic conditions. Hydroponics is a common method of growing crops in CEA, where plants grow in a solution enriched with nutrients and oxygen. The technique significantly reduces water use and fertilizer run-off during production. In the United States, lettuce is one of the most important crops grown using hydroponics.</p> <p> Hydroponic production uses several methods to grow lettuce including nutrient film technique (NFT) and constant flood table (CFT). Moreover, several cultivars of lettuce are grown in the Midwest. There is a lack of knowledge on whether optimal fertilizer concentrations change depending on the cultivar or hydroponic production system. Little information is known about the suitability of a cultivar to a specific method of hydroponic production. For year-round lettuce production in hydroponics, supplemental lighting (SL) and heating are required in the Midwestern regions of the U.S. The energy requirements for SL and heating can be too costly in winter for some growers to produce crop year-round. In addition to light quantity, spectral composition of light can impact growth. Heating the root zone to produce a micro-climate may be more efficient than heating the entire greenhouse and possibly reduce overall heating costs. However, information on spectral composition of light and the efficacy of root zone heating is unclear, at best. Certain cultivars that can tolerate cold stress can be more suitable in the U.S. Midwest during winter. Lettuce cultivar screening for yield under cooler environments is limited. </p> <p> A completely customizable hydroponic production system that can aid in conducting research related to above-mentioned issues was built as a part of my Master of Science program. Using this system, 24 popular cultivars from four lettuce groups were evaluated for productivity during summer/fall under different concentrations of fertilizer solution, and in two production methods including NFT and CFT during spring. In addition, yield of all 24 cultivars were evaluated under 10, 15.5 and 21.1 °C in a growth chamber. The eight best performing cultivars from the summer/fall trial were evaluated during the winter in a greenhouse with the addition of SL and root zone heating with minimal ambient air heating. </p> <p> Results indicated that the lowest level of electrical conductivity (EC) of the fertilizer solution used (1.3 dS·m^-1) resulted in highest yield, regardless of cultivar or method of production. Among the 24 cultivars; Red Sails (Leaf), Salvius (Romaine), Cedar (Oakleaf), and Adriana (Butterhead) had the highest yields among each group during summer. Growth chamber study indicated that Dragoon, Adriana, New Fire Red and Red Sails cultivars had higher yields than other cultivars under cooler (10 and 15.5 °C) air temperature conditions. In the winter study, lettuce cultivars did not reach harvestable size even after 40 days of growth without SL and root zone heating. Supplemental light composition significantly affected lettuce growth with higher yield under Purple (with higher proportion of red) than White LED lighting. Commercially acceptable lettuce could be produced using root zone heating. In general, plants grown under CFT yielded higher than those grown under NFT in the winter trial. Among the cultivars, Salvius, Black Seeded Simpson, Cedar, and Red Sails performed better under SL and root zone heating during winter.</p>

<p>  </p> <p>Many machine learning models exist for battery management systems to utilize. Few have been shown to work. This work focuses on gathering data from cycling battery packs and sending this data directly to machine learning models built off robust datasets for applying the resulting predicted values and outputs directly on top of real-time systems. A parasitic sensor network was created composed of a main microcontroller, a host CPU, and various sensors including resistance temperature detection devices (RTDs), a voltage measurement circuit, current measurement circuit, and an accelerometer/gyroscope. The resulting network was integrated parasitically with a 4-cell 18650 SONY VTC6 battery pack, then tested both on-ground and in-flight with a commercial quadcopter. Real-time data for the battery pack with four cells in series was gathered. This real-time data stream was then integrated with data-driven neural network algorithms trained on various 18650 datasets and a real physical model to finalize the “AI BMS”. Using the power of non-linear models to infer battery health impacts not normally considered in battery management systems, the “AI BMS” was able to use low-fidelity real-time data in conjunction with a powerful multi-faceted model to make predictive decisions about battery health characteristics on top of normal system operations.</p>

<p> Integrating solar energy into the electric vehicle (EV) market alleviates the demand for</p> <p>fossil fuels used to generate the electricity used to power these vehicles. Integrated solar panels</p> <p>provide a new method of power generation for an electric vehicle, but researchers must consider</p> <p>new dependent variables such as drag in the figure of vehicle efficiency. For the solar array to be</p> <p>deemed a viable option for power generation, the solar array must generate enough energy to</p> <p>overcome the added weight and aerodynamic drag forces the solar system introduces. The thesis</p> <p>explores the application of photovoltaic modules for power generation in an EV system.</p> <p>Researchers installed an off-the-shelf solar module on the roof of an EV and investigated the</p> <p>system to explore the efficiency tradeoffs. The research sought to identify an optimized solar</p> <p>panel configuration for minimized drag based on maximized panel surface irradiance, cooling,</p> <p>and array output voltage parameters. The study utilized computational fluid dynamics modeling,</p> <p>wind tunnel testing, and full-scale track testing to analyze the system. The results of this study</p> <p>provide an optimized configuration for a Renogy RNG-100D atop a Chevrolet Bolt. The system</p> <p>was considered optimal at a tilt angle of zero degrees when in motion. The performance benefits</p> <p>due to the increased angle of the solar panel tilt were deemed insufficient in overcoming the</p> <p>aerodynamic drag forces introduced into the system while in motion.</p>

<p>As energy demands for the world increase, the necessity for alternate sources of energy are critical. Just in the United States alone, 92 quadrillion British thermal units (Btu) were used in 2020. As political and geographical pressures surrounding oil increase, along with the growing concern for climate, the drive to explore alternative and renewable means for harvesting energy is on the rise. Solar cells, also known as photovoltaics (PVs), are an attractive renewable source and have been developed as an alternative energy means for over 60 years. When considering losses due to atmospheric absorption and scattering, the Earth’s surface gets about 1000 W/m2 of energy from the sun, which is why there are research efforts around the world trying to maximize the efficiency of solar cells.</p> <p>Organic-inorganic halide perovskites provide for ideal absorbing layers that feature long carrier lifetime and diffusion lengths, strong photoluminescence, and promising tunability. Furthermore, the solution-processing methods used to make these perovskites ensure that the solar cells will remain low-cost and have easy scale-up possibilities. The main problem perovskites is that they degrade in the presence of water, thus leading to decreased device performance.</p> <p>In this work two approaches are investigated to increase moisture stability. The first investigates incorporation of thiols as pseudohalides into the 2D perovskite structure. Instead of the theorized perovskite, two novel 2D compounds were created, Pb₂X(S-C₆H₅)₃ (X= I, Br, Cl) and PbI_1.524(S-C₆H₅)_0.476. While not perovskites, this study gives insight into the effect that the thiol may have on determining structure when comparing –S-C₆H₅ with –SCN groups. Future work will explore more electronegative thiols that will be used to make moisture resistant, tunable 2D perovskites.</p> <p>The second approach is to incorporate longer organic ammonium cations into the perovskite structure to produce quasi-2D perovskite films fabricate them into devices. Adding in electronically insulating ligands leads to a stricter requirement for vertically aligned 2D films and special care must be taken to have efficient charge collection. The current field has successfully incorporated short ligands such as butylammonium (BA) into PVs, however the extension to larger and more beneficially hydrophobic ligands has been very scarce. In this work, a novel solvent engineering system is developed to create vertically aligned quasi-2D perovskite absorbing layers based off of a bithiophene ligand (2T). These absorbing layers are then characterized and incorporated into efficient PV devices. Generalizations to solvent conditions related to ligand choice is discussed herein, creating deep insights into incorporating more conjugated ligands into devices.</p>

<p>Lithium-Ion Batteries (Li-ion batteries or LIBs) have been extensively used in a wide variety of industrial applications and consumer electronics. Additive Manufacturing (AM) or 3D printing (3DP) techniques have evolved to allow the fabrication of complex structures of various compositions in a wide range of applications. </p> <p><br></p> <p>The objective of the thesis is to investigate the application of 3DP to fabricate a LIB, using a modified process from the literature [1]. The ultimate goal is to improve the electrochemical performances of LIBs while maintaining design flexibility with a 3D printed 3D architecture. </p> <p><br></p> <p>In this research, both the cathode and anode in the form of specifically formulated slurry were extruded into filaments using a high-temperature pellet-based extruder. Specifically, filament composites made of graphite and Polylactic Acid (PLA) were fabricated and tested to produce anodes. Investigations on two other types of PLA-based filament composites respectively made of Lithium Manganese Oxide (LMO) and Lithium Nickel Manganese Cobalt Oxide (NMC) were also conducted to produce cathodes. Several filaments with various materials ratios were formulated in order to optimize printability and battery capacities. Finally, flat battery electrode disks similar to conventional electrodes were fabricated using the fused deposition modeling (FDM) process and assembled in half-cells and full cells. Finally, the electrochemical properties of half cells and full cells were characterized. Additionally, in parallel to the experiment, a 1-D finite element (FE) model was developed to understand the electrochemical performance of the anode half-cells made of graphite. Moreover, a simplified machine learning (ML) model through the Gaussian Process Regression was used to predict the voltage of a certain half-cell based on input parameters such as charge and discharge capacity. </p> <p><br></p> <p>The results of this research showed that 3D printing technology is capable to fabricate LIBs. For the 3D printed LIB, cells have improved electrochemical properties by increasing the material content of active materials (i.e., graphite, LMO, and NMC) within the PLA matrix, along with incorporating a plasticizer material. The FE model of graphite anode showed a similar trend of discharge curve as the experiment. Finally, the ML model demonstrated a reasonably good prediction of charge and discharge voltages. </p>

<p dir="ltr">The horizon for inclusion of data-driven algorithms in cyber-physical systems is rapidly expanding due to evermore availability of high-performance computing tools and the inception of novel mathematical models in the fields of deep learning and reinforcement learning. In this regard, energy systems are a suitable candidate for data-driven algorithms utilization due to rapid expansion of smart measuring tools and infrastructure. Accordingly, I decided to explore the capabilities of deep reinforcement learning in control, security, and restoration of smart energy systems to tackle well-known problems such as ensuring stability, adversarial attack avoidance, and the black start restoration. To achieve this goal, I employed various reinforcement learning techniques in different capacities to develop transfer learning modules based on a rule-based approach for online control of the power system, utilized reinforcement learning for procedural noise generation in adversarial attacks against contingency detection in a power system and exploited multiple reinforcement learning algorithms to fully restore an energy system in an optimal manner. Per the results of these endeavors, I managed to develop a rule-based transfer learning logic to control the power system under various disturbance types and intensities. Furthermore, I developed an optimal adversarial attack module using a reinforcement-learning-based procedural noise generation to avoid detection by conventional deep-learning-based detection. Finally for the system restoration, the proposed intelligent restoration module managed to provide sustainable results for the black start restoration in energy system.</p>

No description available.

<p>  </p> <p>  </p> <p>Concentrator photovoltaics have demonstrated greater solar energy production efficiency than previous solar electric technologies.  However, recent research reveals that heat management is a significant difficulty in CPV systems, and if left unaddressed, it can have a severe influence on system efficiency and lifetime. Traditional CPV cooling relies on active methods such as forced air convection, or liquid cooling, which might lead to an extremely large parasitic power use. In addition, the moving parts of a cooling system result in a shorter lifespan and higher maintenance expenses. </p> <p><br></p> <p>CPV systems can boost their efficiency and lifespan by adopting passive cooling solutions. This work employed radiative cooling and natural convection to construct an efficient and cost-effective cooling system. The excess heat of a solar cell can be dispersed into space via electromagnetic waves via radiative cooling. Due to the fact that the radiative cooling power is related to the difference between the fourth powers of the solar cell and the ambient temperature, much greater cooling powers can be obtained at higher temperatures. Heat pipes were installed to act as a heat pump by transferring excessive heat from solar cells within a system to the exterior, where it can be dissipated via natural air cooling and thermal radiation. Experiments conducted in this study demonstrate that a temperature reduction of 21 ◦C was accomplished through radiative cooling and natural convection, resulting in an increase of 64 mV, or 17% in the open-circuit voltage of a GaSb solar cell.</p>